Manoj Agarwal

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About

Manoj Agarwal is a researcher specializing in multi-robot systems, coalition formation, and multi-objective optimization — a niche but critical intersection of artificial intelligence and robotics. His work addresses one of the fundamental challenges in autonomous systems: how groups of robots can effectively organize themselves into coalitions to accomplish complex, real-world tasks. Agarwal's most significant contributions lie in developing sophisticated frameworks for robot coalition formation in non-additive environments, where the combined capabilities of a robot team cannot be simply summed from individual contributions. His 2013 paper on non-additive multi-objective coalition formation, cited 24 times, laid important groundwork in this space, while his 2015 work on parallel multi-objective approaches — his most cited with 35 citations — demonstrated how computational efficiency could be achieved through parallelization in these complex optimization scenarios. His earlier 2011 study helped establish the theoretical foundations that informed his later, more impactful research. Collectively garnering over 60 citations, Agarwal's body of work has meaningfully advanced how researchers and engineers think about autonomous robot teamwork, with implications for search-and-rescue operations, environmental monitoring, and other domains requiring coordinated multi-robot collaboration.